After School Activities and Students Mathematics Achievment

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AFTER SCHOOL ACTIVITIES AND STUDENTS MATHEMATICS

ACHIEVEMENT:

DIFFERENCES BY GENDER, RACE, AND SOCIOECONOMIC STATUS

Susan A. DumaisLouisiana State University

January 14, 2006

AFTER SCHOOL ACTIVITIES AND STUDENTS MATHEMATICS

ACHIEVEMENT:

DIFFERENCES BY GENDER, RACE, AND SOCIOECONOMIC STATUS

Abstract

Using data from the Educational Longitudinal Study of 2002, I examine the extracurricularactivities of tenth-grade students, distinguishing between school-related activities, structuredout- of-school activities, unstructured out-of-school activities, leisure activities, and work. Ianalyze the association between extracurricular participation and mathematics achievement

test scores, both examining all students together, and noting the differences that occur bygender, race/ethnicity, and socioeconomic status. Regardless of background, most studentsspend their time playing interscholastic sports, watching television, and talking on thetelephone. Leisure activities (including television watching and talking on the phone) have anegative effect on math achievement scores, while school-related activities such as studentgovernment and student academic clubs have positive effects. Part-time jobs are found to bedetrimental to all students except for females and African-Americans, for whom employmentactually improves test scores. The findings indicate that, like what Coleman argued in his

Adolescent Society, there is a youth culture focused on leisure in American society, and thisfocus on leisure is associated with lower achievement test scores.

Since the publication of James Colemans The Adolescent Society in 1961,researchers have been interested in teenagers extracurricular activities and

interests, and how the time spent on these activities is related to academic

success. Coleman described the adolescent culture as one where peer

acceptance was critical; he found that participation in extracurricular activities

was one way for students to earn this acceptance from their peers. In Colemans

study, the male jock and the female cheerleader were both held in high regard

by their peers; in fact, a higher percentage of males wanted to be remembered

as a star athlete than as a brilliant student, while girls hoped to be thought

of as attractive or as a leader in student activities. Coleman argued that

teenagers emphasis on extracurricular activities might draw their focus awayfrom academic achievement. Today, extra-curricular activities continue to be

intertwined with the issue of academic success. Most high school students are

aware that to gain admission to a competitive college, they must show that they

are well-rounded, which often means participating in multiple after- school

activities, such as sports, volunteering, or music lessons. Other students may opt

to spend their time hanging out with friends, talking on the phone, or watching

television; the development of computers and video games has led to even more

leisure offerings for bored teens. Still other students work at part-time jobs after

school, either for their own spending money or to help out at home. What effects

do these different kinds of activities have on academic success? Do all activitiesaffect educational outcomes in the same way, or is it more beneficial for a


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student to participate in some types of activities over others? Does The

Adolescent Society of Colemans day still exist, with extracurricular practices

that are antithetical to academic success? Moreover, does participation benefit

everyone equally, or are some groups more likely than others to reap

educational rewards for their extracurricular involvement?

In this paper, I describe the extracurricular activities of a nationally-

representative sample of high school sophomores in 2002, categorizing their

activities as school-related, non-school structured, non-school unstructured,

work, and leisure. I then analyze the effects that these activities have on math

achievement scores in twelfth grade, considering differences that may occur by

gender, race/ethnicity, and socioeconomic status. I find that across all groups,

the same kinds of activities are the most popular. I also find that participating in

certain activities benefits all students, while other activities result in lower math

achievement scores for everyone. Additionally, there are specific activities which

benefit students from different backgrounds in different ways.

BACKGROUND There is a wealth of past research on extracurricular activities and

their effects on various academic outcomes; however, most research focuses on

a specific type of activity, rather than all of the ways that adolescents may spend

their time. Additionally, there is limited research on the effects that gender,

race/ethnicity, and socioeconomic status may have on participation, and on the

effects of that participation; many studies simply include these variables as

controls in their analyses, or choose only one as their research focus. By and

large, the school activity that has received the most research attention is

participation in sports. Some studies of sports participation have also considered

differences by race/ethnicity and gender. Jordan (1999) used data from the

National Education Longitudinal Study (NELS) to examine racial differences in

school sports participation and student achievement. He found that white

students were more likely than black students to participate in team and

individual sports. Participation in team sports had a positive effect on black and

white students grades and achievement test scores; individual sports positively

affected whites, but not blacks, grades and test scores. Eitle and Eitle (2002)

also used the NELS data to examinesports participation for black and white male

high school students. They found that black students were more likely to play

football and basketball, while white students were more likely to participate in

other sports. In contrast to Jordans (1999) findings, Eitle and Eitle (2002) found

that participating in football and basketball was associated with lower scores on

reading and math achievement tests; these decreases appeared for both black

and white participants. Participation in other sports was found to benefit the

grades of white students, but to be detrimental to the grades of blacks. Snyder

and Spreitzer (1977) found that girls who participated in gymnastics had higher

educational expectations than girls who participated in basketball, track, or no

sports at all; however, sports participation explained only between 2-4% of the

variance in girls educational expectations. Using from the High School and

Beyond (HSB) study, Hanson and Kraus (1998) found that boys were more likely

than girls to participate in varsity sports and other athletic teams. Participating insports increased females access to science courses, while sports participation


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television. Gortmaker et al. (1990) had similar findings to Gaddy, using a data

set from an earlier time period. Hofferth and Sandberg (2001) found no

association between television watching and achievement test scores or

behavioral problems; they used time diary data from the Panel Study of Income

Dynamics for children under age 13. Past research thus leaves us with a number

of contradicting findings regarding the effects that extracurricular participationmay have on academic outcomes. Some studies have found positive effects for

sports participation, while others have not; some studies have found television to

be detrimental, while others have not. One thing that the majority of these past

studies have in common is that they focus on only one small slice of a students

life after school; that is, they focus on activities that are school sponsored, or

focus on the effects of a part-time job, but do not consider both activities at

once. At most, studies have considered in-school and out-of-school structured

activities together. However, most teenagers are engaged in a wide variety of

activities after school, and it is important for research to consider the multiple

domains in which students may be spending their time. Students may spend anhour of their afternoons on productive unstructured activities such as leisure

reading, another hour hanging out with their friends, and still another hour

playing in the school band. The present study attempts to improve upon past

research by including all the after-school domains of activity in the analyses,

thus better representing the lives of many American teenagers. Moreover, rather

than focusing onlyon differences by gender, race/ethnicity, or social class, the

present study considers the impact that all three background characteristics may

have on participation rates and the effects of participation on achievement.

ANALYSES Research Questions The following analyses address three main

research questions: 1. Are there differences in the extracurricular participationpatterns of teenagers by gender, race/ethnicity, and/or socioeconomic status? 2.

How does participation in extracurricular activities affect the math achievement

scores of students, and does the effect vary by the type of activity in question?

3. Are there differences in the relationship between extracurricular participation

and math achievement by gender, race/ethnicity, and/or socioeconomic status?

Data and Sample Data are from the Educational Longitudinal Study of 2002

(ELS:2002) base year and first follow-up. These data are sponsored by the

National Center for Education Statistics (NCES) of the Institute of Education

Sciences, U.S. Department of Education, and they are designed to provide trend

data about transitions students make from high school to postsecondaryeducation and early careers. Data were first collected in spring 2002, during the

sophomore year of the respondents; a national probability sample of 752 public,

Catholic, and private schools was selected, resulting in 15,362 sophomore

respondents. 1 Students, parents, teachers, and administrators were all

surveyed. Achievement tests were administered in math and reading; math

achievement was tested again two years later, when most of the students were

seniors. 1 The response rate for schools was 67.8% and the response rate for

students was 87.3%, resulting in a final response rate of 59.2%.

For the analyses that follow, students were included if they had participated in

both the tenth and twelfth grade surveys, if they were white, Black, Hispanic, or


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Asian (other racial groups were too small for meaningful analyses), and if they

were not missing values on any of the key variables. In all analyses, the

appropriate sample weight (F1PNLWT) was used. Results can be generalized to

American high school sophomores in 2002. The dependent variable in the

regression analyses is the twelfth grade score on a mathematics achievement

test administered by NCES. This is currently the only academic outcome variableavailable for the twelfth grade students in the ELS:2002 data. Achievement test

scores have been used in past research on extracurricular activities (Jordan

1999; Broh 2002; Eitle and Eitle 2002). High school transcript data, including

grades and scores on standardized tests such as the SAT, will be forthcoming for

ELS:2002 respondents in the coming months. Students were asked several

questions about their extracurricular participation. For intramural sports

participation, students were asked, For each sport listed below, indicate

whether you participated on an intramural team in this sport during this school

year. The sports listed were baseball, softball, basketball, football, soccer, other

team sport, individual sport (e.g., wrestling, golf, tennis), andcheerleading/pompom/drill team. In the analyses that follow, students are coded

as participating in intramural sports if they said that they participated in one or

more of the sports. The question for interscholastic sports was identical to that

for intramural sports, and the interscholastic sports variable is coded the same

way in these analyses (0 = did not participate in any interscholastic sport; 1 =

participated in one or more interscholastic sports). For the other school activities,

students were asked, Have you participated in the following school-sponsored

activities this school year? The activities were: band, orchestra, chorus, choir;

school play or musical; student government; National Honor Society or other

academic honor society; school yearbook, newspaper, literary magazine; serviceclub; academicclub; hobby club; and vocational education club/vocational

student organization. Students answered yes or no. In the analyses below,

participation in each activity is coded as 1, and nonparticipation is coded as 0. In

addition to school activities, students were asked, How often do you spend time

on the following activities outside of school? The activities were: visiting with

friends at a hangout; working on hobbies, arts, crafts; volunteering or performing

community service; driving or riding around; talking with friends on the

telephone; taking classesmusic, art, language, dance; taking sports lessons;

and playing non-school sports. Responses were: rarely or never; less than once a

week; once or twice a week; and everyday or almost everyday. In the analysesbelow, the coding is as follows:

Visiting with friends at a hangout: everyday or almost everyday = 1, less

frequently = 0Working on hobbies: once or twice a week or more = 1, less

frequently = 0Volunteering: once or twice a week or more = 1, less frequently =

0Driving around: everyday or almost everyday = 1, less frequently = 0Talking on

the phone: everyday or almost everyday = 1, less frequently = 0Taking cultural

classes: once or twice a week or more = 1, less frequently = 0Taking sports

lessons: once or twice a week or more = 1, less frequently = 0Playing non-school

sports: once or twice a week or more = 1, less frequently = 0


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Students were also asked, How much additional reading do you do each week

on your own outside of school- not in connection with schoolwork? Students had

a blank space to fill in the hours. In the descriptive tables below, students who

read for three or more hours a week are coded as 1, and those who read for less

than that are coded as 0. In the regressions, the actual number of hours is used.

Students were also asked about their television watching and video gameplaying habits. In the descriptive tables, students who said they watched 3 hours

or more of television per weekday are coded as 1, and students who said less are

coded as 0; in the regressions, the actual hour amount given is used. In the

descriptive analyses, students who said they played video games for 2 or more

hours per weekday were coded as 1; in the regression analyses, the actual hour

amount was used.

Finally, students were asked how many hours they worked on their current/most

current job (if they had ever worked). Students who answered 10 or more hours

per week were coded as 1 in the descriptive analyses; in the regressions, theactual hour amounts were used. The extracurricular activities analyzed here can

thus be classified into five categories: school activities; non-school structured

activities; work; non-school unstructured activities; and leisure activities. The

classification is as follows: School activities: intramural sports, interscholastic

sports, band/chorus, play or musical, government, honor society,

yearbook/newspaper, service club, academic club, hobby club, vocational club.

Non-school structured activities: community service, cultural classes, sports

lessons, non-school sports. Work: working at a job outside the house for pay.

Non-school unstructured activities: working on hobbies, reading for pleasure.

Leisure activities: hanging out with friends, driving around, talking on thetelephone, watching television, playing video games.

The difference between out-of-school unstructured activities and leisure

activities is that some might argue the unstructured activities are more

productive than the leisure activities. Control variables include the students

score on the tenth grade math achievement test, gender (male = 0, female = 1),

racial/ethnic status (dummy variables are included for Asian, Hispanic, and

Black), school sector (private = 0, public = 1), and socioeconomic status.

Socioeconomic status is a composite variable consisting of parents educational

attainment, occupational prestige, and income in the year 2001. A summary

table listing all of the variables used in the analyses (with their means and

standard deviations) can be found in the Appendix.

Results

Participation Patterns

Table 1 presents the different rates of participation in extracurricular activities

for males and females. Females have higher participation rates in all of the

school activities except for intramural and interscholastic sports, where malesare more likely to participate. For both males and females, sports are the most


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popular of the school activities, with a majority of males and females

participating in interscholastic sports. Participating in school band or chorus is

the most popular non-sports school activity for both males and females. For

males, the least popular activity is the school yearbook or newspaper, with only

5% of males participating. For females, student government, the school

yearbook or newspaper, and vocational clubs are all unpopular activities, withonly about 10% of females participating in each. [TABLE 1 ABOUT HERE] There

are also gender differences in the structured activities in which students engage

outside of school. Forty-seven percent of males report playing non-school sports

at least once a week, compared to 28% of females. Females are more likely than

males to take cultural classes outside of school (28% versus 13%) and to

participate in community service (13% versus 8%). Males are more likely than

females to work ten or more hours at an outside job; 41% of males, compared to

32% of females, fall into this category. Females are more likely than males to

read on their own for three or more hours per week (35% of females, versus 29%

of males, do this), but there are no significant differences in the percentage ofmales and females who spend time on hobbies. Males and females do have some

significant differences in their leisure time: males are more likely to hang out

with friends, watchtelevision, and play videogames, while females are more

likely to talk on the telephone with friends everyday. Overall, the most popular

ways for males to spend their time are interscholastic sports, watching

television, and playing on non-school sports teams, while for females, the most

popular activities are talking on the telephone, interscholastic sports, and

watching television. For both sexes, then, a combination of activity (sports) and

leisure (watching television) seems to be common. Table 2 considers differences

in participation that occur by race and ethnicity. For school-related activities,there are significant differences between the groups for every activity except for

participation in the school yearbook or newspaper. Whites have the highest

participation rates in interscholastic sports, band or chorus, school plays or

musicals, student government, and vocational clubs, while Asians have the

highest participation rates in the honor society, service clubs, academic clubs,

and hobby clubs. Black students have the highest participation rates in

intramural sports. Aside from sports activities (where Asians are the least likely

to participate), Hispanics have the lowest participation rates of all the

racial/ethnic groups in school activities. [TABLE 2 ABOUT HERE] Turning to out-

of-school activities, Asians are the most likely to participate in cultural classes,and the least likely to take sports lessons. Whites have the highest rates of

participation in sports lessons and non-school sports. Whites are also more likely

than the other racial/ethnic groups to work ten or more hours a week; 40% of

whites, compared to 30% of Blacks, 26% of Hispanics, and 21% of Asians, work

this amount. There are no significant differences in working on hobbies or

reading for pleasure, but there are significant differences for every category of

activities in the leisure category. Black students are the most likely to talk on

thephone (64%), watch television for three or more hours a day (68%), and play

video games for two or more hours a day (32%). White and black students are

equally likely to spend time driving around (30%), and whites are the most likelyto spend time hanging out (38%). Asians are the least likely to engage in any of


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these activities. Looking at all the activities together, the most popular activities

are similar across the racial/ethnic groups; the top three activities for whites,

blacks, and Hispanics all include interscholastic sports, watching television, and

talking on the phone. For Asians, talking on the phone is replaced by working on

hobbies, but sports and television are in their top three, as well. Table 3

considers the socioeconomic differences in participation in extracurricularactivities. As a point of reference, the median educational attainment level for

parents in the first SES quartile is a high school diploma or GED, and the median

2001 income is $20,001 to $25,000. For the fourth quartile, the median

educational level is a Masters degree or equivalent, and the median income is

$75,001 to $100,000. For the school-related activities, interscholastic sports,

band/chorus, student government, honor society, service clubs, academic clubs,

and hobby clubs all follow the same pattern: the higher the SES quartile, the

more likely the student is to be a participant. For school plays and musicals, the

third quartile is actually the most likely to participate, and for vocational clubs,

the second quartile (followed by the first quartile) is the most likely toparticipate. The structured activities out of school follow the same pattern as

most of the school related activities: the lowest quartile is the least likely to

participate, and the highest quartile is the most likely.

[TABLE 3 ABOUT HERE]

Students in the second and third SES quartiles are more likely to work 10 or

more hours per week than students in the first and fourth quartiles. The fourth

quartile has the highest percentage of students who read for 3 or more hours a

week outside of school (35%). Studentsin the second and third quartiles are

more likely to hang out and drive around than students in the first and fourth

quartiles, but television viewing and video game playing are most likely in the

bottom quartile, and least likely in the top quartile. This relationship between

parents background and television viewing has been found in past studies

looking at younger children (Bianchi and Robinson 1997). Overall, the most

popular activities for students in the first three SES quartiles are playing

interscholastic sports, watching television, and talking on the phone. For

students in the top SES quartile, the most popular activities are interscholastic

sports, talking on the phone, and working on hobbies. For all groups, then,

interscholastic sports, watching television, and talking on the phone (and in

some cases, working on hobbies) are popular ways to spend ones time when

one is not in class. How does participation in these activities, as well as the other

activities described in Tables 1-3, affect students performance on math

achievement test scores? Effects of Individual Activities on Math Achievement

Table 4 presents the OLS regression results for models predicting math

achievement test scores in twelfth grade. Model 1 includes the control variables.

The students scores on the tenth grade math achievement test, socioeconomic

status, and being Asian all positively affect twelfth grade math test scores, while

being female and attending a public school both have negative effects. The R-

squared for this model is an extremely high .8136, caused mostly by the variable

for the tenth grade math test score; in fact, in a regression with that variablealone, the R-squared was .8074. [TABLE 4 ABOUT HERE] In Model 2, the variables


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for extracurricular participation are included. Of the school activities, student

government, honor society, service clubs, and academic clubs all have a positive

effect on students math achievement scores, while participation in intramural

sports has a negative effect. None of the structured out-of-school activities is

found to have an effect on test scores. The hours a student works at a part-time

job also do not have an effect on the students test scores. Among unstructuredactivities, reading for pleasure has a positive impact on students test scores.

Finally, all of the leisure activities except for video game playing have negative

effects on math achievement test scores; hanging out, driving around, talking on

the phone, and watching television all have significant negative coefficients. The

R-squared for this Model is only a very slight improvement over Model 1, from .

8136 to .8190. When looking at all students together, then, it appears that many

school-related activities can be beneficial to math achievement. While a few of

the activities, such as the honor society and academic clubs, may attract higher-

performing students, other activities like student government and service clubs

are open to all students, and could help them in their future achievement. On theother hand, participation in intramural sports was found to have a negative

impact on achievement scores. Additionally, activities that are part of many

teenagers daily lives, such as watching television or talking with friends, can

bring down students math achievement scores. The previous two models did not

take into account differences that might occur by students background

characteristics. In the next three tables, I consider the interactions that gender,

race/ethnicity, and socioeconomic status may have with extracurricular activities

and their effects on achievement. Interactions with Gender, Race/Ethnicity, and

Socioeconomic Status Gender. Table 5 shows the results of a regression run with

interaction terms for each of the extracurricular activities and female status.Models 1 and 2 are identical to Models 1 and 2 of Table 4. In Model 3, tenth

grade math scores, socioeconomic status, and being Asian all continue to affect

twelfth grade math achievement positively; being female, being black, and

attending public school all have a negative effect. The negative effect for being

black was not present in Models 1 and 2, and the negative effect for being

female is less statistically significant than it was in the previous two models.

Among the extracurricular activities, participating in intramural sports continues

to have a negative effect, while student government, honor society, and

academic club participation all improve math test scores. Service club

participation, which had positively affected math scores in the previous model,has no effect on Model 3. None of the structured non-school activities is found to

have an effect. Working negatively affects math test scores, while reading for

pleasure no longer provides the benefit that it did in Model 2. Among the leisure

activities, hanging out, driving around, and watching television all continue to

negatively affect test scores, but the coefficient for talking on the phone is no

longer significant. [TABLE 5 ABOUT HERE] Turning to the interaction terms,

female * intramural sports, female * outside sports lessons, and female * hours

worked all positively affect test scores, while female * phone negatively affects

test scores. This means that variables that by themselves negatively affect test

scoresparticipation in intramural sports and working an outside jobare not asdetrimental for females as they are for males. In fact, working at an outside job


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actually becomes beneficial for females after taking the interaction term into

account. On the other hand, talking on the phone does not affect males either

positively or negatively, but it has a negative effect on females, who are far more

likely to talk on the phone everyday. The R-squared for this model (with the

interaction terms) is .8200, indicating that very little additional variance in math

achievement test scores is explained by including interaction terms for genderand extracurricular activities. Race/Ethnicity. The next table includes interaction

terms for each of the racial/ethnic groups Blacks, Hispanics, and Asiansand

each of the activities. Comparing the control variables in Model 3 to those in

Model 2, the main difference is that in Model 3, the coefficient for Asian is no

longer statistically significant; being Asian does not have a positive effect on test

scores, as it did in previous models. For the school-related activities,

participating in intramural sports has a negative effect on test scores, as it did in

Model 2; participation in the honor society, service clubs, and academic clubs all

continue to positively affect test scores, and the coefficient for student

government becomes statistically insignificant. Additionally, hobby clubs arefound to improve test scores in this model. Of the structured non-school

activities, playing non-school sports has a positive impact on math test scores;

none of the other activities in this category affect the dependent variable.

Working at an outside job has a negative effect on test scores, while reading for

pleasure continues to have the positive effect that it did in Model 2. All of the

leisure activities except for playing video games negatively affect students math

test scores. [TABLE 6 ABOUT HERE] For black students, the only interaction term

to affect math test scores was black * hours worked at an outside job; this

interaction term was positive and significant, meaning that black students who

worked actually benefited from their job: the coefficient of the interaction term, .031, was than the coefficient for the variable hours worked at outside job

(-.014). For Hispanics, the interactions Hispanic * student government, and

Hispanic * cultural classes were both positive, while the interactions of Hispanic *

hobby clubs, Hispanic * vocational clubs, and Hispanic * non-school sports were

all negative. Both the variables school hobby clubs and non- school sports had

positive coefficients, meaning that other students who participated in those

activities would score higher on their math tests; for Hispanic students, the

negative coefficients of the interaction terms were large enough to wipe out any

benefits from those activities. None of the activities were found to interact with

being Asian in a significant way. The R-squared for this model was .8207.Socioeconomic Status. The results for the regression run with interaction terms

for each of the extracurricular activities and SES are presented in Table 7.

Models 1 and 2 are the same as in the previous tables; the results of the

interactions are shown in Model 3. The control variables in Model 3 are all similar

to Model 2 except for the variable for Black, which is now statistically significant;

being Black lowers ones math achievement test score. Among the school-related

extracurricular activities, student government, the honor society, school service

clubs, and academic clubs all continue to positively affect the math test score, as

they did in Model 2; participation in intramural sports continues to have a

negative effect. Participation in community service outside of school has anegative effect in this model, as does the time spent working at a job. The


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unstructured activities and leisure activities all remain largely unchanged from

Model 2, with all leisure activities except for playing video games having a

negative effect on math achievement scores; talking on the phone and the hours

of television watched both increase in statistical significance in this model. Time

spent reading for pleasure continues to have a positive impact on achievement.

[TABLE 7 ABOUT HERE] In Model 3, the only significant interaction terms arehonor society * SES, which has a negative coefficient and non-school sports *

SES, which has a positive coefficient. In other words, lower-SES students who

participate in the honor society score higher on their math achievement test

than higher-SES students; in contrast, non-school sports participation is more

beneficial to higher-SES students than to lower-SES students. The coefficient for

honor society participation is positive and significant, meaning that all students

who participate in honor society receive an academic benefit; the interaction

term indicates that lower-SES students who participate will benefit more than

high-SES students. The coefficient for non-school sports participation was not

significant; only the interaction of non-school sports and SES is. For the mostpart, then, socioeconomic status does not interact with extracurricular activities.

In the cases where it does, it does not appear to benefit one group over another;

the lowest group benefited from one activity (honor society), while the highest

group benefited from another (non-school sports). Overall, Model 3 shows that

the activities that provide the most benefit to students test scores are those

that are school affiliated; those activities that are most detrimental to students

test scores are the leisure activities such as hanging out or talking on the

telephone. The overall R-squared for the model was only a very slight increase

over the previous model, from .8190 to .8197. DISCUSSION This study addressed

three main research questions. First, I asked whether there were different ratesin extracurricular participation by gender, race/ethnicity, and/or social class.

While significant differences in extracurricular participation were found between

males and females, the different racial/ethnic groups, and the different

socioeconomic quartiles, with two exceptions, all groups were similar in the

activities that were most popular: playing interscholastic sports, watching

television, and talking on the telephone. The two exceptionsAsians and

students in the highest socioeconomic quartilehad working on hobbies as one

of their most popular activities, rather than talking on the phone, but they both

also had playing interscholastic sports and watching television in their top three

activities. Unfortunately, the most popular activities were not the ones thatprovided the academic benefits to students. My second research question asked

how participation affected students math achievement test scores. The analysis

(with all students included) showed that the school- related activities of student

government, honor society, service clubs, and academic clubs all were

associated with higher scores on the math achievement test. Intramural sports

were the one school-related activity that had a negative effect. No non-school

structured activities wererelated to higher test scores; this is in some ways good

news, because it means that some students are not missing out on a path to

higher achievement because they do not have the resources or time to

participate in an outside activity. Reading for pleasure, a non-schoolunstructured activity, resulted in higher test scores, while working at a part-time


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job resulted in lower test scores. Finally, many of the leisure activities teens

enjoywatching television, hanging out, talking on the phone, and driving

aroundsignificantly lowered math achievement scores. Thus, two of the most

popular activities for teens, watching television and talking on the phone, led to

lower test scores. Of the activities that were beneficial, higher SES students were

more likely to participate than the other SES groups, and females were morelikely to participate than males. Asians were the most likely to participate in

some of the activities (honor society, academic clubs, and service clubs), while

whites were the most likely to participate in others (student government). Blacks

and males were the most likely to participate in intramural sports, which had a

negative effect on test scores, and blacks, students in the lowest SES quartile,

and males were all more likely than their peers to watch a lot of television, which

was associated with negative test scores. The third research question asked how

the effects of participation might vary by students background characteristics.

One of the more interesting findings was that while working at a part-time job

was detrimental to the achievement of many students, it actually boosted theachievement of females and Blacks. Future research should explore why this

relationship exists; perhaps it has to do with the types of part-time jobs that

females and black students hold. Additionally, the models with interactions

showed that females, who were more likely to spend time talking on the phone

than males, were also the ones who were most negatively affected by it in their

math achievement. Hispanic students also received fewer benefits from some

activities (non-school sports, hobby clubs) than their peers. Overall, however,

there werenot any clear patterns showing advantages for one group over

another in participating in activities. In all of the models, both with and without

interaction terms, the activities which were most beneficial to students mathtest scores were concentrated in the school-sponsored realm; reading for

pleasure was one unstructured activity with consistently positive effects. The

most detrimental activities throughout the models were in the leisure realm;

part-time employment negatively affected many students, too, but not females

or African-Americans. Throughout the analyses, participation in intramural sports

had a negative effect. Broh (2002) found this in her research, as well. Her study

found that compared to interscholastic athletes, intramural athletes did not

accrue social capital or developmental benefits (such as self- esteem and locus

of control) from their participation, which in turn hurt their achievement. While

school sector was used as a control variable in the analyses, further researchshould explore school context more closely, as has the work of researchers

examining school- related extracurricular activities (Guest and Schneider 2003;

McNeal, Jr. 1999). Additionally, as stated above, other outcome measures, such

as grades, and college enrollment, should be used for dependent variables in

future studies, once the data become available. American teenagers spend their

after-school hours in a wide variety of ways. Many of them combine the

competitiveness and energy of interscholastic sports with several hours per day

sitting on the sofa, watching television. Opting to spend less time in front of the

tube and more time participating in school-related activities, such as student

government, may give them an academic edge over their peers. It appears thatColemans Adolescent Society, with its focus on hanging out and having fun,


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After School Activities and Students Mathematics Achievment